Research on Multimodal Feature Enhancement Method and Its Applications

With the rapid development of computer technology, deep learning has become a research hotspot in recent years. Feature extraction, as a key component of deep learning, plays a vital role in improving model performance. However, single-modality methods often suffer from low accuracy in complex real-world scenarios. To address this challenge, we apply a multimodal feature enhancement method to practical scenarios, and experimentally verify its superiority over single-modality approaches. The proposed framework employs a Conformer encoder for feature encoding. The encoded audio-visual features are fused by Multilayer Perceptron (MLP), then decoded into text sequences via a Transformer decoder, thus improving accuracy and robustness. Additionally, the model is trained on a custom dataset to enhance generalization. This study focuses on evaluating the method’s practical performance, providing insights for technology implementation. Experiments demonstrate that the proposed method achieves superior practicality in multimodal feature extraction, with notable real-time advantages for real-world applications.